Personalized vasopressor therapy in patients with septic shock

感染性休克患者的个体化升压药治疗

• 批准号: 10506084
• 负责人: SETH BAUER
• 金额: $ 17.63万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10506084
• 关键词: Achievement; Age; Biological; Blood Pressure; Cessation of life; Characteristics; Clinic; Clinical; Clinical Investigator Award; Clinical Pharmacists; Consolidated Framework for Implementation Research; Critical Care; Critical Illness; Data; Data Analytics; Data Science; Data Set; Decision Modeling; Development; Disease; Doctor of Philosophy; Electronic Health Record; Environment; Epinephrine; Ethnography; Failure; Functional disorder; Goals; Health; Health system; Heterogeneity; Hospitals; Hypotension; Individual; Infection; Inflammatory; Infrastructure; Intensive Care Units; International; Intervention; Interview; K-Series Research Career Programs; Knowledge; Leadership; Left Ventricular Ejection Fraction; Life; Logistic Regressions; Machine Learning; Mediating; Mentorship; Methods; Modeling; Modernization; Norepinephrine; Outcome; Output; Patient Care; Patient-Focused Outcomes; Patients; Perception; Pharmaceutical Preparations; Pharmacodynamics; Pharmacology; Positioning Attribute; Prediction of Response to Therapy; Race; Recording of previous events; Regimen; Research; Research Project Grants; Risk; Role; Savings; Scientist; Sepsis; Septic Shock; Shock; Supportive care; Surveys; Techniques; Testing; Training; Translational Research; Translations; Treatment Failure; United States; Vasoconstrictor Agents; Vasopressins; Work; base; behavior change; care delivery; clinical implementation; clinical practice; clinical translation; clinically translatable; cohesion; comorbidity; dosage; implementation framework; implementation science; improved; individual patient; individualized medicine; informatics infrastructure; innovation; model development; mortality; novel; patient response; predictive modeling; pressure; response; risk prediction model; septic patients; sex; sociodemographics; stakeholder perspectives; theories; therapy outcome; tool; treatment effect; treatment research; treatment response; treatment strategy

PROJECT SUMMARY/ABSTRACT Septic shock has a mortality rate above 40%, and is treated with supportive care including vasopressors. Yet, evidence shows vasopressor treatment response and clinician practices are heterogeneous. The key factors mediating this heterogeneity are unknown. This research project investigates the role of patient-specific factors in treatment response to initial and adjunctive vasopressor therapy in patients with septic shock. We seek to develop accurate, individualized tools to predict the heterogeneity of response to a variety of vasopressors in individual septic shock patients and implement these tools into bedside practice. Our preliminary data show early initiation of adjunctive vasopressin is associated with lower mortality, and individual patient and treatment characteristics are associated with vasopressin responsiveness. Therefore, we propose the novel hypothesis that clinically translatable models are capable of predicting early vasopressor treatment response and will optimize initial and adjunctive vasopressor therapy to improve clinical outcomes. This hypothesis will be tested through three interrelated, but independent specific aims: (1) to apply machine learning techniques to predict initial vasopressor therapy response; (2) to develop a decision model for optimizing adjunctive vasopressor therapy outcomes; and (3) to identify determinants of successful vasopressor prediction model clinical translation. Our proposal is innovative in concept because it is the first to use cutting-edge data science methods to develop prediction models for initial and adjunctive vasopressor treatment effects. The proposed research is significant because it has the potential to decrease mortality in septic shock patients by developing and implementing personalized vasopressor treatment approaches. Cleveland Clinic is the ideal environment to complete the proposed research due to the high volume (>6,000 yearly) of clinically and socio- demographically diverse sepsis patients, integrated multi-professional treatment and research teams, extensive informatics infrastructure, large cohesive health-system with a shared electronic health record, and history of successful evidence translation into improved care delivery. As a critical care clinical pharmacist, Dr. Bauer is uniquely positioned to develop and implement innovative vasopressor treatment strategies through knowledge of the combined influences of medication pharmacology and pharmacodynamics, patient comorbidities, and disease pathophysiology on treatment effects. With the mentorship of Dr. Vidula Vachharajani and other internationally-recognized experts, Dr. Bauer will pursue training goals to enhance his understanding of applied clinical translational science, clinical prediction modeling, implementation science, and research leadership. Pursuit of these aims and training goals through the K08 Career Development Award mechanism will facilitate achievement of Dr. Bauer’s long-term goal of becoming an independent clinician-scientist who improves the health and outcomes of patients with septic shock through the optimized utilization of vasopressor therapies.

项目总结/摘要 感染性休克的死亡率超过40%，需要支持性治疗，包括血管加压药。然而， 证据显示血管加压药治疗反应和临床实践是异质的。的关键因素 介导这种异质性是未知的。本研究项目调查患者特异性因素的作用 在感染性休克患者中对初始和后续血管加压治疗的治疗反应。我们寻求 开发准确的，个性化的工具来预测对各种血管加压剂反应的异质性， 个别感染性休克患者和实施这些工具到床边实践。我们的初步数据显示，早期 开始使用抗利尿激素与较低死亡率、个体患者和治疗相关 特征与加压素反应性相关。因此，我们提出了新的假设， 临床上可翻译的模型能够预测早期血管加压药治疗反应， 优化初始和后续血管加压治疗，以改善临床结局。这一假设将得到检验 通过三个相互关联但独立的具体目标：（1）应用机器学习技术来预测 初始血管加压药治疗反应;（2）开发用于优化治疗反应的决策模型 血管加压药治疗结果;（3）确定成功的血管加压药预测的决定因素 典型的临床翻译我们的提案在概念上是创新的，因为它是第一个使用尖端数据的提案 科学的方法来开发预测模型的初始和连续的血管加压药治疗效果。的 拟议的研究是重要的，因为它有可能降低败血性休克患者的死亡率， 开发和实施个性化的血管加压治疗方法。克利夫兰诊所是理想的 环境，以完成拟议的研究，由于大量（>6,000年）的临床和社会， 人口统计学上多样化的脓毒症患者，整合的多专业治疗和研究团队，广泛的 信息学基础设施，具有共享电子健康记录的大型凝聚力卫生系统，以及 成功地将证据转化为更好的护理服务。作为一名重症监护临床药剂师，鲍尔博士 独特的定位，通过知识开发和实施创新的血管加压治疗策略， 药物药理学和药效学、患者合并症和 疾病病理生理学对治疗效果的影响。在Vidula Vachharajani博士和其他 国际公认的专家，鲍尔博士将追求培训目标，以提高他对应用的理解 临床转化科学、临床预测建模、实施科学和研究领导力。 通过K 08职业发展奖励机制追求这些目标和培训目标将有助于 Bauer博士的长期目标是成为一名独立的临床科学家， 通过优化血管加压药治疗，改善感染性休克患者的健康和预后。